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Aerodynamic performance and blockage investigation of a cambered multi-bladed windmill

2020· article· en· W3087942231 on OpenAlexaff
Itoje H. John, Jerson Rogério Pinheiro Vaz, David Wood

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSolidityAerodynamicsAirfoilRotor (electric)WindmillThrustStructural engineeringTorqueWind tunnelMarine engineeringEngineeringAerospace engineeringComputer scienceWind powerMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Windmills for water pumping typically operate at low speed and high torque owing to their multi-bladed nature. This, however, complicates the rotor aerodynamic behavior due to the mutual interaction between adjacent blades and low Reynolds number, Re, operation. While studies on their aerodynamic performance indicate that increasing blade number, N, and airfoil type are critical analysis and design parameters, its low Re behavior with cambered airfoils appears complicated and is still poorly understood. Accordingly, the performance of a windmill model of diameter 0.68 m with 3 ≤ N ≤ 24 identical blades was investigated in two open jet wind tunnels, with different test section sizes: one with high blockage of 36.3 % and the other with a negligible blockage of 4.5 %, for comparison with Blade Element Theory (BET) predictions of thrust, torque, and power. It was found that BET is accurate except at low tip speed ratios, λ where it under-predicts the torque primarily because of the high solidity at high N. Furthermore, the study reveals that high blockage impacts significantly on rotor performance and is a function of N . Overall, the experiment gave a better performance, highlighting the importance of accounting for solidity in aerodynamic performance prediction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.189
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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